Human Intestinal Microbiota in Response to Antibiotics, Nicotine and Caffeine

Previous work shows that intestinal microbiota varies significantly from individual to individual with changes in diet, exercise, smoking and a myriad of other factors resulting in changes to microbial diversity. Thus, fundamental to development of any therapeutic intestinal microbiota treatments, we need to have a mechanistic understanding of how bacterial diversity are impacted by these factors. The goal of this project was to see how behavioral factors influence both the antibiotic driving decline of gut microbiota but also the recovery period following an antibiotic course.

Stool sample collection was conducted over 26 days with 16 sample points along with detailed diet description was gathered. DNA extraction from all sixteen samples was done by an automated Maxwell16 system using a Maxwell 16 Tissue DNA purification kit. Several methods were then performed to analyze the microbial community. First, microbial populations were compared by examining differences in G/C content of PCR products following amplification with primers for the high variable V3 region of the 16S ribosome gene. PRC products were visualized using Denaturing Gradient Gel Electrophoresis (DGGE) which allows for separation based on annealing strength of DNA fragments. Dendrogram band pattern relatedness from DGGE was analyzed using GelCompare II, v6.6 11 (Applied Maths, Austin, TX) based on the Dice similarity coefficient and the unweighted pair group method using arithmetic averages (UPGMA) for cluster analysis and comparison according to percentage similarity coefficient (%SC). Extracted DNA samples were also sent for outside sequencing (Research and Testing Laboratories, Lubbok, TX). Samples were amplified using a primer covering the V3 and V4 regions of the 16S ribosome gene and sequenced on the Illumina platform. The sequences were then submitted to the RDP classifier to identify bacterial species. A taxonomic matrix was assembled using an 80% cut-off threshold to the lowest possible taxonomic level. This was analyzed using non-metric multidimensional scaling (NMDS) in R Studio (R Studio, Boston, MA).

As expected NMDS data (Fig.1) indicates that the microbiome for the 16 samples is split into two groups based on the antibiotic course [1]. Sample 1 was collected on the same day as the antibiotic regimen began, before there would be time for antibiotics to accumulate and impact the intestinal microbiota. S2, collected 24 hrs. after S1 shows how rapidly the microbiome changes in response to antibiotics. Likewise, S9 and S10 are also transition points showing how rapidly discontinuation of antibiotic treatment results in changes of species composition (Figure 1).

An anomaly here is sample S15 which is unexpectedly grouping with the antibiotic-treated samples and not with the untreated samples. The prior samples (S13 and S14) aren’t showing any shift like the transition points S9 and S10. We suspect that the S15 is being driven by a lack of sleep. The day prior to collection of S15 the subject reported only 4 hours of sleep and lack of sleep has been reported to have an effect on the gut microbiota [2].

We found that the antibiotic treatment had the strongest effect on the microbiome community with samples collected during the course of antibiotics grouping together in ordination space and samples collected after the course of antibiotics had completed also grouping together.

None.

No Conflicts of Interest.

1. Pérez Cobas AE, Gosalbes MJ, Friedrichs A, Knecht H, Artacho A, et al. (2013) Gut microbiota disturbance during antibiotic therapy: a multiomic approach. Gut (11): 1591-1601
2. Benedict C, Vogel H, Jonas W, Woting A, Blaut M, et al. (2016) Gut microbiota and glucometabolic alterations in response to recurrent partial sleep deprivation in normal-weight young individuals. Molecular Metabolism 5(12): 1175–118